Learning Exactly Linearizable Deep Dynamics Models

• Published in: arXiv.org
• Main Authors: Moriyasu, Ryuta, Kusunoki, Masayuki, Kashima, Kenji
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 30.11.2023
• Subjects: Automotive engines; Dynamic models; Linear control; Machine learning; Performance prediction

Research on control using models based on machine-learning methods has now shifted to the practical engineering stage. Achieving high performance and theoretically guaranteeing the safety of the system is critical for such applications. In this paper, we propose a learning method for exactly linearizable dynamical models that can easily apply various control theories to ensure stability, reliability, etc., and to provide a high degree of freedom of expression. As an example, we present a design that combines simple linear control and control barrier functions. The proposed model is employed for the real-time control of an automotive engine, and the results demonstrate good predictive performance and stable control under constraints.